Eye position registering and tracking

ABSTRACT

Embodiments of the invention refer to a system for registering and tracking the position of a person&#39;s eye, in particular for refractive ophthalmic surgery. According to embodiments, the system is structured such that eye images containing at least the iris and the pupil of the eye are made at a first wavelength of light and that eye images containing scleral blood vessels are made at a different second wavelength of light. The invention furthermore refers to a corresponding method for registering and tracking the position of a person&#39;s eye.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 10/499,498, filed on Dec. 23, 2002, now U.S. Pat. No. ______,issued ______, the disclosure of which is herein incorporated byreference.

BACKGROUND

This disclosure relates to a system for registering and tracking theposition of a person's eye, in particular for refractive ophthalmicsurgery, comprising:

-   -   a camera system (18) for taking images of the person's eye;    -   storage means connected to the camera system (18) for storing at        least one eye image as a reference eye image;    -   an image processing system connected to the storage means and        the camera system (18) for comparing a momentary eye image with        the reference eye image and for outputting a signal representing        a change of eye position between the reference eye image and the        momentary eye image.

Such systems are e.g. used in refractive ophthalmic surgery, i.e. insurgical operations in which the cornea of a patient's eye is shaped bya laser beam in order to correct for defects of vision. Before thesurgical operation, a measurement of the patient's eye is made with thepatient usually sitting in an upright position while focussing on atarget image. A so-called wavefront analyzer or other refractivediagnostic device, such as corneal topographer or refractometer, thenobjectively determines an appropriate wavefront correction for reshapingthe cornea of the eye. Typically the wavefront analyzer calculates acylindrical or quasi-cylindrical ablation profile, which is to beapplied to the eye by means of a focussed laser beam.

During the refractive surgery operation, the patient usually lies on hisback, i.e. his head is typically in another position than duringdiagnosis with the wavefront analyzer. In order to correctly apply thepreviously calculated ablation profile to the eye, it is thereforeessential to correctly register the position of the person's eye, i.e.to determine the translational and rotational displacement of the eyewith respect to its position during diagnosis, so that the system“knows” how to apply the previously calculated ablation profile to thecornea in its momentary position.

Not only changes of eye position between diagnosis and surgery, but alsoeye movements during the operation have to be taken into account. Inrefractive laser systems, the laser operates in fixed space coordinates.The patient's eye is classically stabilized by voluntary visual fixationof a visual target. However, eye movements cannot be eliminatedcompletely by voluntary fixation and furthermore slower head movementsstill occur during the surgery procedure, both changing the eye'sposition relative to the laser. However, with the increasing demands ofcustomized corneal ablation, in particular the use of smaller beamsizes, faster repetition rates and greater precision of correction,exact positioning of each single laser shot onto the eye has becomeincreasingly more important. This need for greater positioning accuracyhas provided the impetus for several refractive laser companies toimplement eye tracking systems into their surgical systems in order toposition the ablation beam accurately onto the corneal surface and tocompensate for patient head and eye movements during the operation. Manyeye tracking systems track the image of the pupil. However, the exactposition of this image depends on the refraction of light through thecornea in front of the pupil. The amount of corneal tissue through whichthe light passes may change due to the orientation of the eye, leadingto an artificial shift of the image position, which negatively affectsthe tracking.

When markers are applied to the eye and tracked instead of or inaddition to the pupil, other problems arise: The markers may irritatethe eye or require anesthesia. In other cases, the attachment of themarkers to the patient's eye may not last much longer than for exampleone hour, therefore imposing a time limitation to the operation andpermissible time between refractive measurement and correction

Document WO 01/78584 A2 discloses a system for registering and trackingthe position of a person's eye according to the preamble of claim 1. Forregistering purposes, this system compares the limbus centers of the eyein the reference eye image corresponding to its position duringdiagnosis and in the momentary eye image, and calculates a translationaldisplacement of the eye from the difference of the limbus centers.Furthermore, the position of other eye features like e.g. retinal bloodvessels, scleral blood vessels or a retinal nerve are compared betweenthe reference image and the momentary image in order to determine therotation of the eye between its diagnosis position and its surgeryposition.

In this prior art system, the eye is basically illuminated by daylight,and the cameras used to take pictures of the eye are “ordinary” camerasfor collecting color images. All eye features necessary for registeringare therefore visible in each image. However, as all eye features arealways illuminated by daylight, the contrast in particular of bloodvessels is poor. The registering setup described in the above prior artdocument, however, is too slow for use in a clinical situation in whichlonger operation times result in less income, in particular due to thefact that the poor contrast of the essential eye features in all colorimages renders the localization of these features time-consuming.

Another limitation of the prior art system is that it does notdifferentiate between blood vessels that are stable relative to the eye(cornea) and vessels which are stable relative to the head. As it ispossible for the patient's eye to rotate independently of the head, thisleads to some vessels moving relative to the cornea (surgical targetarea) at varying rates. Blood vessels may move at different ratesdepending on the relationship between rotation of the head, rotation ofthe eye, depth of the vessel in the conjunctive or sclera and positionof the vessel relative to the eye lids or cornea. Therefore the abovedescribed method may result in incorrect or inaccurate results, due tothe non-differentiation between vessels that move with the eye, andvessels that move with the head.

It is therefore an advantage of the invention to provide a faster, moreaccurate system for registering and tracking the position of a person'seye.

According to some embodiments, this advantage is achieved by a systemthat allows images of the iris/pupil region of the eye to be taken at adifferent wavelength of light than images of the scleral blood vessels,so that both wavelengths can be set to suitable values in order toenhance the contrast of the respective eye feature.

As an example, in an embodiment of this system according to theinvention the first wavelength lies in a wavelength range betweenpreferably 810 nm and 880 nm, but can be also between 780 nm and 950 nmand the second wavelength lies in a wavelength range between preferably520 nm and 570 nm but can also be 500 nm and 580 nm. In this case, thefirst wavelength corresponds to infrared (IR) light, which is known tobe very suitable for enhancing the iris/pupil contrast. The secondwavelength corresponds to green light enhancing the contrast of bloodvessels.

In order to further increase the contrast of the scleral blood vessels,the second wavelength can be set to a local maximum of an absorptionspectrum of hemoglobin. As is well known, this maximum lies at awavelength of approximately 550 nm, corresponding to green light.

In order to produce the two kinds of images with different wavelengths,an embodiment of the system according to the invention comprises a firstlight source system illuminating the iris and the pupil of the eye and asecond light source system illuminating the sclera of the eye. In thiscase, the first light source system emits light of the first wavelength,whereas the second light source system emits light of the secondwavelength.

As to the question how these two light source systems co-operate and howthe two kinds of images are made with light of different wavelengths,various approaches are conceivable:

In one embodiment of the system according to the invention, the firstand second light source systems are controlled such as to simultaneouslyilluminate the respective parts of the eye. In this case, the patient'seye is simultaneously illuminated with light of two differentwavelengths arriving from two different light source systems: Thecentral part of the eye, namely pupil and iris are illuminated with afocussed IR beam, whereas the sclera of the eye is simultaneouslyilluminated with green light.

At least as far as eye tracking during a surgical operation isconcerned, it can be assumed that the amplitude of eye movements issufficiently small to make sure that no misalignment problems regardingthis spatially structured arrangement of light sources will occur. Bothlight source systems can therefore be stationary. The spatial emissionprofile of the IR light source system then basically corresponds to alight cone having a diameter of approximately 10 to 11 mm, making surethat no IR light reaches the eye outside the limbus. In a similar way,the light emission profile of the second light source system must bedesigned such as to make sure that no green light of the secondwavelength falls on the iris and pupil.

A particularly flexible adjustment of the spatially structured beamsfrom the two light source systems can be obtained when the systemaccording to the invention furthermore comprises light directing meansfor variably directing the light of the first light source system and/orthe second light source system to the respective parts of the eye. Aspreferable examples of such light directing means, a scanning mirrorand/or a movable lens can be mentioned. The use of such optical devicesfor precisely directing light beams on a target, in this case the regioninside or outside the limbus, is well-known and will therefore not bedescribed in detail.

As an alternative to the above-discussed example of a system using aspatially structured beam, another embodiment can comprise multiplexingmeans for controlling the first and second light source systems such asto alternately illuminate the entire eye. In other words, such anembodiment does not use spatially structured beams, but rather twoseparate larger beams of different “color” which are structured in time.As an example, the multiplexing means can choose the first light sourcesystem at first, so that the entire eye is illuminated with IR light.The camera system takes a momentary IR image of the entire eye, in whichin particular the iris/pupil region has a strong contrast and in whichalmost no blood vessels are visible. This momentary IR image can bestored in the storage means. Then the multiplexing means switch from thefirst to the second light source system which then illuminates theentire eye with green light. Again a momentary image of the entire eyeis made, in which in particular the blood vessels have a strongcontrast. This image can also be stored in the storage means. The imageprocessing system can then calculate any translational displacement ofthe eye with respect to the reference image based on the IR image of theeye allowing a precise localization of the pupil. Then a possiblerotation of the eye with respect to the reference image can becalculated based on the green image allowing a precise localization ofthe scleral blood vessels.

In yet another embodiment of the invention, the image processing systemis designed such as to subtract an image which is recorded at the secondwavelength of light from the second light source system from a precedingimage recorded at the first wavelength of light from the first lightsource system. The remaining “difference image” is even more dominatedby the scleral blood vessels than the second image itself, which stillenhances the precision of the blood vessel localization.

In view of a suitable pupil/iris contrast, the first light source systemcan be arranged such as to illuminate the eye at an angle ofapproximately 30° with respect to the visual axis. In particular anillumination of the iris and pupil from approximately 30° below thevisual axis has turned out to be suitable in practical operation.

Whereas the first light source system, which basically has to produceone single IR light cone, can consist of a single IR light source,realization of the second light source system is more difficult, as ithas to illuminate two separate regions of the eye, namely the sclera onthe left and right side of the iris. In an embodiment of the inventionsuitable for fulfilling this illumination requirement regarding thesecond wavelength light, the second light source system comprises twolight sources arranged such as to symmetrically illuminate the eye atangles of approximately +35° and −35°, respectively, with respect to thevisual axis.

In order to further improve the quality of the images made, i.e. toenhance the contrast of the pupil/iris in the IR images and the contrastof the scleral blood vessels in the green images, additional measurescan be taken in view of the camera system: Thus, in an embodiment of theinvention, the camera system can be only sensitive to light of the firstand the second wavelength. In this case, daylight in the operating roomdoes not negatively affect the images.

In practice such an arrangement can be obtained when the camera systemcomprises one single camera provided with a double-passband filter.Alternatively the camera system can comprise a CCD camera having acombination chip with at least two separate color bands corresponding tothe first and the second wavelength, respectively.

As yet another alternative, the camera system can comprise a firstcamera sensitive only to the first wavelength and a second camerasensitive only to the second wavelength.

Embodiments of the invention furthermore relate to a method ofregistering and tracking the position of a person's eye, in particularfor refractive ophthalmic surgery, comprising the steps:

-   -   recording an image of the person's eye in a reference position;    -   storing the image as a reference eye image;    -   recording at least one momentary eye image;    -   calculating a change of eye position between the reference eye        image and the at least one momentary eye image; and    -   outputting a signal representing the change of eye position        characterized in that the step of recording at least one        momentary eye image comprises recording a first image containing        the iris and the pupil of the eye at a first wavelength of light        and recording a second image containing scleral blood vessels at        a different second wavelength of light, and that the step of        calculating the change of eye position comprises calculating a        displacement of the scleral blood vessels between the reference        eye image and the second image. As explained above, the first        wavelength can be optimized in view of an optimum contrast of        the iris/pupil for determining a translational displacement of        the eye, whereas the second wavelength can be set such as to        optimize the contrast of the scleral blood vessels for        determining the rotational displacement. As explained above, the        first wavelength therefore preferably corresponds to IR light,        and the second wavelength preferably corresponds to a local        absorption maximum of hemoglobin.

In a further embodiment of the method according to embodiments of theinvention, it comprises the step of extracting scleral blood vesselsfrom the second image, and in still another embodiment it also comprisesa step of classifying extracted blood vessels according to theirstability and tractability. The subtraction step can be performed as hasbeen described above, i.e. by subtracting the green image from the IRimage. Other possible methods comprise selected enhancement/filtering,the use of matched filters and (multi)thresholding, the use ofanisotropic filters and (multi)thresholding and in particularaccumulated watershed segmentation. The watershed technique has a biastowards fully enclosed features. A method for removing this bias is toartificially add features to the image, such as a black grid, to enhanceconnectedness or circularity of the features, and then perform thewatershed segmentation. The added artificial feature is then removedfrom the result, leading to a far less biased watershed segmented image.Repeated watershed segmentation with decreasing height level revealsincreasingly finer vessels. This provides a stable estimate of the widthof the vessels, since wider vessels create deeper valleys in the grayscale image and can be segmented in more height levels.

Criteria for the classification for the detected blood vessels comprise,among others, the question if a feature is a blood vessel and not forexample an eye lash or other artifact, and if a blood vessel belongs tothe sclera or to the conjunctiva. This classification can be done basedon properties of the vessels, such as appearance, location, thickness,focus, connectedness of the vessels, vessel shape direction or contourand intensity/contrast or contrast changes along the length of thevessel. For example, it may be possible to distinguish blood vesselsfrom an eye lash based on the straightness, length and direction (e.g.±30° from the vertical) or focus of the feature.

As an alternative to the extraction and classification of blood vessels,the method according to the invention can comprise defining an area ofinterest in the reference eye image and calculating a maximumcorrelation coefficient between the area of interest and the secondimage.

According to a further embodiment the present invention deals with theproblem of how to locate in an image of an eye those area or areas whichcontain blood vessels so that these regions can be used for eitherregistration or eye tracking. While there are probably many areas in aneye picture which contain picture elements representing blood vessels,it would be helpful for the purpose of eye tracking or eye registrationif one or more regions are chosen where the blood vessels are present insuch a manner that they are particularly suitable for tracking orregistration.

According to an embodiment of the invention there is provided a methodfor eye registration or eye tracking based on an initial or referenceimage and a momentary image, said method comprising: obtaining one ormore of so called landmarks in said initial image containing image datawhich are likely to represent blood vessels or parts thereof; and basedon said landmarks, selecting one or more regions of interest as parts ofsaid initial image which are to be used for eye tracking orregistration. According to this embodiment the landmark selection makesit possible to select areas (regions of interest) in the initial imagewhich are particularly suitable for tracking or registration.

According to a further embodiment for each of said regions of interest,there is obtained a displacement measurement between said initial imageand said momentary image; and if multiple regions of interest are used,said multiple displacement measurements are combined to obtain a finaldisplacement measurement. This makes it possible to take into accountthat despite the selection of regions of interest which are particularlysuitable for tracking, each individual measurement may be erroneous, andby using multiple measurements the accuracy can be increased.

According to a further embodiment the step of obtaining said landmarkscomprises one or more of the following: performing a Fouriertransformation based on said initial image and selecting as saidlandmarks pixels or groups of pixels which have a high intensity in thefrequency domain;

convoluting said initial image with one or more templates representingan edge or a corner or a curve in order to select from said convolutedimage or images such areas in which edge, corner or curve structureshave been detected; or

calculating orthogonal gradients for said initial image and selectingthe regions of interest based on said gradients calculated. Theaforementioned methods of landmark selection make it possible to performthe landmark selection automatically. This makes it easier for the userto apply the method of the invention. The mentioned methods all give anindication where in the reference image there are contained structureswhich may be suitable for tracking, in other words they give anindication about where it is likely that blood vessels are present, andbased on this indication there can then be selected the regions ofinterest in an automatic manner which is more convenient than a manualselection of the regions of interest. These methods are based onassumptions about how suitable blood vessels or parts thereof shouldlook like, and the mentioned image processing methods are “sensitive” tosuch structures and therefore can be used for the automatic extractionof landmarks.

According to a further embodiment of the present invention there isprovided a method which first calculates based on an initial orreference image two gradient images for orthogonal gradient directions.These gradient images give an indication of how strong or how steep theimage intensity changes along the two orthogonal directions, and theytherefore already give kind of a rough indication about the presence ofblood vessels because blood vessels are areas where there should be somekind of image gradient in at least one direction.

The method according an embodiment of the invention then furtherperforms a mathematical operation based on that two gradient imageswhich makes sure that there is at least a minimum gradient in both ofsaid orthogonal directions. This makes it sure that there is an imageintensity change in both orthogonal directions, this means that there issome certainty that the image structure indicated by these gradients isnot just for example a blood vessel extending only into the x- or they-direction, but rather it is a structure which shows an intensitychange (and thereby an image structure) along two orthogonal directions.This is particularly helpful because for the purpose of the detection ofrotation angles for eye tracking or eye registration purposes thestructure used for registration or tracking must be such that it is notonly one-dimensional.

According to a particularly preferred embodiment the mathematicaloperation which ensures that there is a minimum gradient in bothorthogonal directions uses a mathematical approach which ensures thatthe intensity change is independent from the coordinate system. For thatpurpose there is used a covariance matrix based on the two gradientimages. There is formed a covariance matrix for a certain predefinedwindow around each pixel of the reference image, and based on thiscovariance matrix the eigenvalues are calculated. These eigenvaluesrepresent the image gradients in two orthogonal directions in a mannerindependent of the coordinate system, i.e. in a covariant manner.

By taking the minimum eigenvalue for each predetermined window aroundeach pixel of the reference or initial image it can be made sure thatthe thus selected eigenvalue gives an indication about the minimumgradient of the image in both two orthogonal directions.

The aforementioned method can be applied for all pixels of the referenceimage, thereby obtaining a minimum eigenvalue for each of the pixels ofthe reference image, whereas each minimum eigenvalue thus calculatedgives a representation about how strong at least the image gradient isin two orthogonal directions. Because especially for the purpose ofrotation measurement it is important that the selected features (and theregions of interest selected which contain the suitable features) havegradients in both orthogonal directions to make a rotation measurementpossible. This can be achieved by the aforementioned method of using theminimum eigenvalues calculated for each of the pixels of the referenceimage.

Based on the thus obtained eigenvalue image according to one embodimentone can then select those areas of the reference image which can beassumed that they contain particular suitable structures (blood vessels)for the purpose of tracking or eye registration. These regions (or evenindividual pixels) may be called landmarks, and they are selected basedon the minimum eigenvalue image. For example, one can at first choosewhich has the maximum eigenvalue among the eigenvalue image pixels, andthen define a region of interest around this selected pixel. This willthen give the first region of interest for the purpose of eye tracking.

After having blanked out the thus selected pixels of the first region ofinterest one can again go through the eigenvalue image of the referenceimage and can select the next highest eigenvalue. Around this pixel onecan form a second area of interest, and the procedure may then berepeated either until a suitable number of areas of interest has beenobtained, or for example until the eigenvalue corresponding to a certainpixel of the reference image falls below a certain threshold.

Of course it is also possible to imagine other ways of selecting orextracting so-called “landmarks” in the initial image, where saidlandmarks can be assumed to contain suitable blood vessel structures.One can for example take groups of pixels, e.g. of 5×5 block size fromthe eigenvalue image, calculate their average intensity, and based onthese values select those blocks for which the average intensity isrelatively high, e.g. by applying a threshold, by selecting the n blockswith the highest average intensity, or the like.

The thus selected “landmarks then form the base for the “regions ofinterest” which are then used for a comparison between the referenceimage and the momentary image for tracking or registration. The regionsof interest typically are areas, which are chosen such that theysurround the selected “landmarks”, e.g. by forming a predefined windowaround them.

According to a further embodiment one can then use the thus obtainedpredetermined regions (areas of interest) for the purpose ofdisplacement measurement. For that purpose one compares each area ofinterest in the reference image with a momentary image to look how muchthe area of interest must be shifted to find again the area of interestin the momentary image (to look for a “match”). This can for example bedone by calculating a correlation measurement for each shifting ordisplacement value within a certain predetermined window around theregion of interest, and this then will lead to a map or an “image” ofcorrelation values where each pixel corresponds to a certaindisplacement of the reference image and indicates the correlationmeasurement for this displacement.

According to a preferred embodiment the calculated matching score (thecorrelation measurement) is further weighted by a weighting value whichindicates the “directionality” of features in the reference image likeblood vessels. This can for example be done by applying anisotropicsteered filters such as a bank of Laplacien of Gausian (LoG) filters.This will then given an indication about the “directionality” offeatures for each of the pixels of the reference image, and it will kindof “enhance” those structures for which there is a strong directionalitylike in the case of blood vessels which are long and slim in shape, i.e.have a strong directionality.

Based on the weighting then there is obtained a matching score map foreach of the regions of interest, whereas each pixel of the matchingscore map indicates the matching score for a particular displacement ofthe reference image based on the correlation value calculated for thisshift and weighted with the weighting map.

According to a preferred embodiment the matching scores are thenaccumulated for the individual displacement values and for the multipleregions of interest to thereby obtain a shift value which is most likelyto represent the actual shift value.

This accumulation of multiple matching score maps takes into account andto some extent corrects several effects which may negatively influencethe measurement for the individual regions of interest. For example,individual blood vessels may shift their position independently of theeye movement just due to their instability. Moreover, the measurementitself may be erroneous. These effects can be at least to some extent betaken into account and be corrected by accumulating the matching scoresfor the individual regions of interest.

According to a preferred embodiment furthermore an a priori knowledgeabout the correlation coefficient and the probability that the eyedisplacement actually takes the measured value is used to replace thecorrelation measurement by a corresponding probability. Using thisprobability thus obtained there is then calculated the accumulatedprobability for each of the individual displacements based on themultiple regions of interest for which the correlation map has beencalculated.

This will then finally give a maximum probability for one of thedisplacement values which can then be taken as the final displacementvalue obtained from the measurement.

According to a further preferred embodiment the imposition uncertaintywhich is introduced by the measurement error is also taken into accountby computing for each position the accumulated probability of itsneighbors.

According to a further embodiment it is also possible to furtherclassify the selected landmarks or regions of interest according to theis suitability for tracking. This can be done by any knownclassification method or method of supervised learning like neuralnetworks such classification techniques or supervised learningtechniques may also be themselves used for the selection of thelandmarks or regions of interest. This can e.g. be done by successivelyclassifying regions of interest in the initial image using a classifyersuch as a neural network or any other method of supervised learning andclassification.

BRIEF DESCRIPTION OF THE DRAWINGS

Further aspects and advantages of preferred embodiments of the inventionwill now be illustrated with reference to the accompanying drawings, inwhich:

FIG. 1 is a schematic view of an embodiment of the system according tothe invention in which the first and second light source systemssimultaneously illuminate different parts of the eye;

FIG. 2 is a graph illustrating the spatial structure of the beamilluminating the eye in the embodiment according to FIG. 1;

FIG. 3 is a schematic view of the system according to FIG. 1 duringcorneal ablation;

FIG. 4 is a schematic view of a second embodiment of the systemaccording to the invention, in which the two light source systemsalternately illuminate the entire eye.

FIG. 5 shows an image of an eye.

FIG. 6 shows a process flow according to an embodiment of the invention.

FIG. 7 shows a method of displacement measurement according to anembodiment of the invention.

FIG. 8 shows scleral blood vessels in an initial image.

FIG. 9 shows the feature map corresponding to the image of FIG. 8.

FIG. 10 shows illustratively landmarks in an initial image and amomentary image.

FIG. 11 shows the predetermined regions respectively surrounding theLandmarks of FIG. 8.

DETAILED DESCRIPTION

FIG. 1 shows a first embodiment of a system 10 according to theinvention serving for registering and tracking the position of apatient's eye 12.

The system comprises a first light source 14 emitting IR light in thedirection of the iris and the pupil of the eye 12. Furthermore thesystem 10 comprises two second light sources 16 a, b emitting greenlight in the direction of the sclera of the eye 12.

As can be clearly seen in FIG. 1, the three light sources 14, 16 a,bsimultaneously illuminate the eye 12: green light from the second lightsource 16 a impinges on the left part of the sclera on the left side ofthe iris, IR light from the first light source 14 simultaneouslyimpinges on the iris itself, and another cone of green light from thesecond light source 16 b illuminates the right part of the sclera on theright side of the iris.

The reflections associated to these three cones of light are directed toa camera 18 by means of a movable camera mirror 20. As an example, thiscamera is a CCD camera having a combination chip with two separate colorbands corresponding to the IR light from the first light source 14 andthe green light from the second light sources 16 a,b. In other words,the camera 18 simultaneously takes two pictures of the eye 12, namely anIR image which almost exclusively shows the iris/pupil region, and agreen image which almost exclusively shows the sclera of the eye 12 witha particularly sharp contrast of the scleral blood vessels.

The spatial structure of the light impinging on the patient's eye 12 isshown in FIG. 2 as a graph of light intensity as a function of an eyecoordinate. One can clearly recognize the central IR light cone and thetwo neighboring cones of green (or blue) light impinging on the scleraand its blood vessels.

The camera 18 digitizes the two images made and sends the correspondinginformation to an image processing system 22 via a data line 24.

The image processing system 22 compares both images to a reference eyeimage stored in storage means (not shown in the figures). The comparisonbetween the IR image and the reference eye image allows to determine anytranslational displacements of the eye 12, whereas the comparisonbetween the green image containing the blood vessel positions and thereference eye image allows to determine any rotational displacements ofthe eye 12, as will be described in detail below.

The image processing system 22 outputs a signal representing the changeof eye position to a scanner device 26 via a data line 28. Based on thissignal, the scanner device 26 modifies the position of a movable lasermirror 30, which is to reflect a laser beam from a laser 32 to the eye12 for ablation purposes. With the scanner device 26 correctlycontrolling the movable laser mirror 30, eye movements detected by theimage processing system 22 can be taken into account either forregistration or for tracking purposes.

FIG. 3 shows the system 10 according to FIGS. 1 and 2 during the finalstep of corneal ablation. The scanner device 26 has slightly tilted themovable laser mirror 30 with respect to its original position in FIG. 1,and the laser 32 emits a laser beam which is reflected towards the eye12 by the slightly tilted laser mirror 30. The tilt of the mirror 30compensates for all translational and rotational changes of eye positionas schematically indicated by a small rotation of the eye 12 in FIG. 3with respect to FIG. 1. The IR light source is omitted here for clarityreasons.

FIG. 4 shows a second embodiment of the system 10 according to theinvention. In this second embodiment, the first light source 14 and thetwo second light sources 16 a,b alternately illuminate the entire eye.In FIG. 4, the first light source 14 is omitted for clarity reasons.FIG. 4 shows a situation, in which the two second light sources 16 a,bare active, so that in the situation shown in FIG. 4, the camera 18 onlymeasures a “green” image. As explained above, this image can itself becompared to the reference eye image by the image processing system 22 inorder to determine eye rotations based on positional changes of thescleral blood vessels, or the green image made during the situation ofFIG. 4 can be subtracted from a preceding IR image in order to yield adifference image which basically only contains blood vessel information.All other components of the second embodiment of the system 10 accordingto the invention correspond to its first embodiment and will thereforenot be described in detail any more.

Hereinafter we will now in somewhat more detail an image processing forthe purpose of tracking or registration according to an embodiment ofthe invention.

FIG. 5 shows an image of the eye which can form the starting point forthe method of image processing used in eye registration or eye tracking.The image shown in FIG. 5 may be taken two times, once as a reference orinitial image as a starting point, and then later on as a momentaryimage where the eye has somehow shifted or displaced itself whencompared with the reference image.

Assuming that FIG. 5 shows the reference image we will now proceed withthe explanation of the method according to an embodiment of the presentinvention.

First of all there is performed a 2D-centration/registration of theimage. This means that at first there is looked for the eye center. Thiswill then later help to extract the scleral area based on the anatomicalgeometry of the eye, and it furthermore can also be used for the purposeof delivering an initial 2D-translation between the two images(reference image and momentary image).

For the purpose of centration/registration it is assumed that the eyecenter can be approximated by either the limbus or the pupil center. Thepupil center can be calculated using the center of gravity of a convexhull, or object segmented using intensity thresholding or alternatively,though transform or elliptical fit of the edge points. This list ofmethods is not exhaustive and other techniques well known in the artcould be used. The limbus center can be calculated by utilizing thegradient in pixel intensity along a radial line from the pupil center.Using a predetermined gradient threshold, the edge points of the limbuscan be found and an elliptical fit algorithm applied to find the limbuscenter.

Both of these functions are actually auxiliary only for the purpose ofblood vessel tracking and rather form an initial starting point for theimage processing to be performed later on.

For the purpose of easier image processing and easier calculation of thedisplacement value there is then next performed a polar coordinatetransformation. In particular for the purpose of torsion measurement thetransformation into polar coordinates is very helpful because it makesthe calculation much easier. As a matter of course this transformationrelies on a correct identification of the eye center. However,inaccuracies in the center can be compensated at the end of theprocessing, as they are easily detectable by inaccuracies in 2Dregistration (equal torsion values of contrary sign) on the left andright side of the image. The transformation can for example be performedfor image areas which are situated on the left and the right side of theiris in an approximated range of

θ×R=[−pi/4:pi/4]×[6 mm:11 mm].  (1)

In the transformation bilinear interpolation is used as known to theperson skilled in the art. Any pixels which fall outside the image spaceare defaulted to zero.

It should be noted that if hereinafter x,y-coordinates are mentioned,that then this may either relate to cartesian coordinates or to polarcoordinates, the latter actually being more practical especially fortorsion measurement.

As a further preparation step the invalid regions of the eye image aremarked or masked. First of all the so-called cornea reflections aredetected. These are spots in the eye which are particularly bright, muchbrighter than the surrounding areas. To find and mark or mask thoseareas the following approach can be taken.

First of all a median filter is applied to the image as follows:

$\begin{matrix}{I\overset{20 \times 20\mspace{11mu} {filter}}{\rightarrow}I_{median}} & (2)\end{matrix}$

Next the difference between the median image and the reference iscalculated:

I _(diff) =I−I _(median)  (3)

As a next step then there are found those locations for there exists ahigh difference (with T as a fixed parameter):

{X,Y}={(x,y)|I _(diff)(x,y)>T},  (4)

Those pixel locations thus found can be assumed to be cornealreflections and they have to be either taken out or “smoothed out”. Thiscan for example be done by replacing the found pixel with non-corruptedvalues by the following approach:

I(X,Y)=I _(median)(X,Y)  (5)

As a next step then there is performed a masking step which segments thevalid regions, i.e. selected regions within the sclera area. It can beassumed that the vessels in the sclera region are on a white backgroundand therefore the contrast is relatively high.

In order to extract or segment the scleral region the following methodcan be used. First of all, it is assumed that the scleral region has abetter reflectivity than the eye lids. This means that the eyelid-sclera border then creates gradients in the image. As a consequence,the brightness varies spatially, and based on this initial assumptionthe scleral region can be extracted by the following method.

First of all there is computed a global weighted image mean withstronger emphasis on gradient points:

Val _(global)=mean(I*grad(I)),

Grad_(global)=mean(grad(I))  (6, 7)

Thereby grad(I) is some gradient operator, such as steerable LoGfilters. A particular embodiment of such a filter will later on bedescribed in more detail.

Then the image is divided into a set of non-overlapping subregions (e.g.3×3 pixels), and then for each region the same parameter as mentioned inequations (6) and (7) is calculated:

Val _(local)=mean(I _(local)*grad(I _(local))),

Grad_(local)=mean(grad(I _(local)))  (8, 9)

Next then there is a threshold applied for each subregion

$T = \frac{{Val}_{global} + {\alpha*{Val}_{local}}}{{Grad}_{global} + {\alpha*{Grad}_{local}}}$

α thereby is a predefined parameter for weighting the influence of thelocal statistics.

Based on the threshold then there can be decided whether thecorresponding pixel belongs to the sclera or not, and depending on thatit is either a sign a 0 or a 1 thereby forming a mask which masks thenon-scleral region.

Assuming that some of morphological irregularities may occur during thebefore mentioned procedure, these spurious regions or holes may beeliminated by suitable morphological operations such as opening andclosing.

Moreover, the morphological operations may be applied to eliminate theborder pixels close to eye lids and limbus. These morphologicaloperations of opening and closing and erosion are know to the personskilled in the art and therefore are not further described here.

Based on the foregoing operations now there has been obtained a maskwhich masks the scleral region as the starting point for the furtherprocedure.

The next step then relates to the finding of those parts of the scleralregion which contain image features which are particularly suitable forimage tracking. This means that those pixels or groups of pixels have tobe found where image information about blood vessels is not only presentbut also is present in a manner which is particularly suitable fortracking. The result of this step will be that several so-calledlandmarks have been found where one can assume that the correspondingimage information in the reference image not only is related to thepresence of blood vessels but also is in a manner which makes itsuitable for image tracking and registration.

This step of the method of the present embodiment is based on theassumption that a good tracking quality can be obtained only if thelandmark has significant gradients on orthogonal directions. It istherefore at first based on the initial reference image applied agradient operator which leads to two gradient images, one in eachorthogonal direction. This step can be mathematically expressed asfollows:

$\begin{matrix}{I\overset{{Sobel}{({7 \times 7})}}{\rightarrow}\left\{ {G_{x},G_{y}} \right\}} & (11)\end{matrix}$

Two resulting gradient images then give a first indication about thegradients of the reference image in orthogonal directions. However,according to the method of the present embodiment not only the gradientin one direction is decisive, but rather the particularly good area forimage tracking should have significant gradients in both orthogonaldirections. It therefore is looked for image parts where the gradientsin both orthogonal directions are significant.

According to one possibly embodiment one could for each of the pixels ofthe gradient images look for the minimum of the two gradient values, anduse this minimum value as the pixel value for a resulting final “minimumgradient image”. Then each pixel in this final minimum gradient imagewould represent the minimum gradient in the two orthogonal directionsfor each pixel of the reference image.

However, according to a particularly preferred embodiment there is useda slightly different approach which uses a covariant expression takinginto account the two gradient images. For that purpose there is for eachof the pixels of the reference image formed a covariance matrix over ablock of size of 32×32 pixels centered in each pixel of the referenceimage. This can be mathematically expressed as follows:

$\begin{matrix}{H = \begin{pmatrix}{\sum{G_{x}G_{x}}} & {\sum{G_{x}G_{y}}} \\{\sum{G_{x}G_{y}}} & {\sum{G_{y}G_{y}}}\end{pmatrix}} & (12)\end{matrix}$

The covariance matrix then is a formulation which is coordinate systemindependent and which nevertheless takes into account the imagegradients into the two orthogonal directions over a block surroundingeach pixel of the reference image by a certain predetermined size.

As a next step then there are computed the eigenvalues of the covariancematrix H, and for each of the pixels of the reference image there arethus obtained two eingen values. To obtain a final image representingthe quality of the gradients involved direction (the final gradientimage) or quality image I_(quality) there is then chosen for each of thepixels the minimum eigenvalue corresponding to the matrix whichcorresponds to this pixel. This can be mathematically expressed asfollows:

I _(quality)(x,y)=min(λ₁ ^(x,y),λ₂ ^(x,y))  (13)

The thus obtained quality representing image is an indication for eachof the pixels of the reference image as to how strong there are thegradients, or better to say, the minimum gradient in two orthogonaldirections in this image point. For those pixels where this value isparticularly high, it can be assumed that this is a good image partwhich contains image features suitable for tracking. Therefore, at firstthere is chosen the maximum value of this image as a starting point fora first region of interest suitable for tracking. This can be done asfollows:

MaxV=max(I _(quality))  (14)

This maximum value then gives a first landmark or a first image partwhere one can assume that there are features suitable or particularlysuitable for image tracking.

One can then draw a region of interest as a predetermined windowsurrounding the thus found landmark.

Then there is conducted a search for further landmarks. This can forexample be done by looking for other pixels in the quality image wherethe pixel value is high, for example the next highest value from themaximum. In order to make sure that the thus obtained next landmark isnot too close to the initially obtained value there are first at allneighbours of the initial maximum value set to 0, for example on a rangeof a minimum distance of 16 pixels. Then there is looked for the nextmaximum value in the quality image.

The aforementioned procedure is briefly explained as a flow chart inFIG. 6. First of all the two gradient images are calculated, then thereis calculated the covariance matrix for each pixel of the referenceimage based on the two gradient images. Then based on the covariancematrix the eigenvalues are calculated, and then for each pixel of thereference image there is chosen the minimum eigenvalue to obtain aquality image representing the “feature quality” with respect to itssuitability for the purpose of image tracking. Based on the thusobtained quality image there are selected picture elements or regions aslandmarks which are suitable for image tracking. A “landmark” thereforemay be a pixel selected from the quality image, or it may be an areasurrounding a pixel selected from the quality image, or it may be anarea selected from the quality image. In the embodiment describedhereinafter a landmark is a pixel and the region of interest is an areasurrounding it, however, the region of interest may as well be directlyselected based on the quality image for example as an area having thehighest average intensity.

Assuming that the selected landmark is a pixel in the quality image,then for each of the thus selected landmarks there is chosen acorresponding region of interest, for example by selecting a predefinedsurrounding area for each landmark. This region of interest is then thebasis for the calculation of a correlation factor between the referenceimage and the momentary image taken at a later stage. For that purposeit is looked then later how much the region of interest has to beshifted od displaced from its position in the reference image to matchwith its position in the momentary image. The most easy approach wouldbe to just calculate the difference value between the region of interestat the momentary image and the reference image for each possibledisplacement value. Because the possible movement of the eye is somewhatlimited, there can however be set a predetermined area (a kind of a“displacement window” surrounding each region of interest within whichone can assume that the eye movement should be. For this predeterminedregion or displacement window there can then be calculated thedifference between the momentary image and the reference image for theregion of interest for each possible displacement value. This isschematically illustrated in FIG. 7. Within the eye 1200 in thereference image there is defined a predetermined window (displacementwindow) 1210 within which the region of interest 1220 determined basedon the landmark extraction can move. For the momentary image 1250 thereare then within the predetermined region (the displacement window) 1260calculated differential images for each possible displacement of theregion of interest 1270. This is indicated schematically by the dashedversions of the regions of interest 1270.

This then results in a differential image for each possible displacement(within the displacement window), and for the actual displacement onecould for example use such a displacement value for which thedifferential image forms a minimum, e.g. by adding up all pixels of thedifferential image and looking for the displacement value for which thissum is the minimum.

While this would be quite a simple approach, according a preferredembodiment of the present invention, a more sophisticated approach canbe taken.

At first, according to a preferred embodiment the landmarks found by themethod explained before are not the only input for the calculation ofthe final correlation value. Rather, there is a further input which isbased on the assumption that those areas of the reference image whereblood vessels are present should be weighted more heavily in calculatingthe final correlation value.

For that purpose first of all there is calculated a weighting map whichassigns a weight for each of the pixels of the reference image. Thosepixels or areas where blood vessels are present should be weighted moreheavily than other areas.

Assuming that vessels are dark thin bandlike structures with cleardirectionality, one can find these vessels or find a presentation of theimage where they show up as enhanced features by applying an operatorwhich enhances these features. One example for such a directionalityenhancing operator is a bank of LoG anisotropic steerable filters.

In a preferred embodiment of the present invention five filters are usedwith equidistant orientation in [0:pi/2] range. The used standarddeviations for the gaussians are σ_(d), σ_(s)=1.5, 3, where d stands forderivation and s for smoothing.

The image may then be applied to the filter band as follows:

F _(i) =I

LoG_(i), for i=1:5  (15)

The output of the application of the filter may then be used as themin/max difference as follows:

FM(x,y)=max_(i)(F _(i)(x,y))−min_(i)(F _(i)(x,y))  (16)

Thereby the base LoG filter (of orientation 0) is given by:

$\begin{matrix}{{{{LoG}\left( {\theta = 0} \right)} = {{g(x)}*\frac{\partial^{2}{g(y)}}{\partial y^{2}}}},{{where}\mspace{14mu} g\mspace{14mu} {is}\mspace{14mu} {the}\mspace{14mu} {Gaussian}\mspace{14mu} {function}}} & (17)\end{matrix}$

The steered filters are obtained by rotating the base version by anangle θ:

LoG(θ)=rot_(θ)(LoG(0))  (18)

This then results in an image which is a kind of feature image or afeature map, where for each pixel of the reference image there isobtained a weighting value which lies between 0 and 1 and which is anindication as to how likely it is that the pixel contains or belongs toa blood vessel.

Based on the found landmarks and the found weighting map (the featuremap) one can then initiate the landmarks used for image tracking. Asmentioned already before the landmarks are extracted based on thecovariance matrix and its eigenvalues, and then for each selectedlandmark there is defined a region on interest surrounding it. Thisresults in a multiple regions of interest, and for each of them within adisplacement window a displacement measurement is performed.

For each of these multiple regions of interest there is then obtainedthe corresponding feature map or weighting value map. As anotheralternative one can before performing the actual matching calculate thefeature map, one then further can calculate the landmarks and theirsurrounding areas (regions of interest), and these regions of interestare the areas for which the actual matching is to be performed. Theseregions of interest therefore are “templates” which actually define theareas within which the actual reference image and the feature map(weighting map) are used for displacement measurement, and theytherefore may be stored as templates in advance after their calculationbased on the initial image. For the area of the templates then later thecorrelation value is calculated for such displacement values where thetemplates are within a certain predefined window (displacement window).

These two images or templates (one for the actual reference image, onefor the feature map) corresponding to the multiple regions of interesttherefore form the input for the actual matching process.

The matching process itself is now described in more detail in thefollowing.

FIG. 8 shows as an example a fraction of the reference image containingblood vessels, and FIG. 9 shows as an example the weighting image orfeature map obtained from the part of the reference image by using thebefore mentioned method. It can be seen that in the weighting image theblood vessel structures clearly are enhanced and therefore heavierweight will be given to the blood vessel structures when calculating thefinal correlation measure.

For the purpose of eye registration or eye image tracking there has tobe calculated a displacement value which indicates how much themomentary image is displaced from the original reference image. Thisdisplacement value is calculated based on the regions of interest whichhave been obtained based on the extraction of the landmarks.

For each of the regions of interest there has been obtained by thelandmark extraction there is defined a surrounding area (a displacementwindow) for example as follows. To the left and to the right thesurrounding area is 10 pixels wider than the region of interest obtainedby the landmark extraction. In the vertical direction the predeterminedarea the predefined area is for example so many pixels higher than thecorresponding block as is represented by 15° to each side. Assuming thatthe selected landmark is a block of 32×32 pixels, then this results in apredefined window of 52 pixels in width and 152-162 in height.

The region of interest obtained based on the landmark extraction then isused as a template and is shifted within the predefined area (thedisplacement window) such that it still completely lies inside it.

For each of the templates there is then performed a matching between theinitial image and the momentary image. This is schematically illustratedin FIG. 10. FIG. 10 shows the landmarks in the initial image (left) andthe momentary image (right). FIG. 11 then illustrates that the actualmatching is performed for regions of interest which surround thelandmarks. These are shown as rectangles in FIG. 11.

Then the matching between the template and the underlying block of themomentary image (B_(xy)) is computed as follows:

MS(x,y)=ms(T _(image) ,T _(weight) ,B _(xy)),B _(xy) ⊂ROI  (19)

Thereby MS(x, y) represents a matching score function which is describedbelow.

Assumed that T_(image) is the image part of the reference image whichcorresponds to the selected region of interest based on the landmarkextraction. Furthermore, assumed that T_(weight) is the correspondingpart of the weighting image (the templates). Then the procedure is asfollows.

First of all T_(image) is normalized and also B is normalized. Thereby Bis the momentary image.

Then there is computed a weighted statistics for T_(image) based onT_(weight). This is then by computing usual statistics taken intoaccount the importance of each pixel. This can be mathematicallydescribed as follows:

$\begin{matrix}{m_{w} = {\frac{1}{N}{\sum\limits_{x,y}{{I\left( {x,y} \right)}*{W\left( {x,y} \right)}}}}} & (20) \\{{std}_{w} = {\frac{1}{N}{\sum\limits_{x,y}{{{{I\left( {x,y} \right)} - m_{w}}}*{W\left( {x,y} \right)}}}}} & (21)\end{matrix}$

Next then there is computed the contrast as follows:

c=std _(Tweight)(T _(image))/std _(Tweight)(B)  (22)

Then there is computed the difference image between the momentary imageB and the reference image T_(image) by taking into account thestatistics as follows:

I _(diff) =c*(B−m _(Tweight)(B))−(T _(image) −m _(Tweight)(T_(image)))  (23)

Then there is computed the matching score as:

$\begin{matrix}{m_{pq} = {\sum\limits_{x,y}{x^{p}x^{q}{I\left( {x,y} \right)}}}} & (24)\end{matrix}$

The normalization of the images mentioned before has the purpose ofremoving the illumination differences. It is based on the assumptionthat the illumination differences can be approximated to planersurfaces. The normalization is performed as follows:

First of all the algebraic moments of the orders m₀₀, m₀₁, m₁₀ for agiven image are calculated as follows:

$\begin{matrix}{m_{pq} = {\sum\limits_{x,y}{x^{p}x^{q}{I\left( {x,y} \right)}}}} & (25)\end{matrix}$

Then the contribution of the corresponding polynomials is subtracted:

$\begin{matrix}{I_{N} = {I - {\sum\limits_{p,q}{m_{pq}P_{pq}}}}} & (26)\end{matrix}$

Where the used polynomials are

P ₀₀=1,P ₁₀ =x,P ₀₁ =y  (27)

The resulting matching score gives for each displacement value of theregion of interest within the predefined window a corresponding matchingscore and thereby it results in a matching score map, where each pixelelement corresponds to a displacement value.

Such a matching score map is obtained for each of the regions ofinterest (each template) which have been obtained by the landmarkextraction.

This means that there has been obtained a plurality of maps of matchingscores, each map corresponding to a certain region of interest, and eachgiving an individual estimate for a certain displacement value whichlies within the predefined window (displacement window).

In the next step these matching scores or matching results areaggregated or accumulated to obtain a final matching score for theindividual displacement values to then obtain a final displacementvalue. This is carried out based on the assumption that the measurementof the displacements is influenced by measurement errors, moreover,there is also probably an influence of unstable blood vessels. Theseinfluences when accumulating the individual measurement results shouldcancel out, and by using the most likely measurement result one shouldget a good approach for the actual displacement value.

Therefore, there is followed a maximum likelihood approach for thetorsion a kind of an optimum estimator.

First of all the individual matching scores are transformed intocorresponding probability values. This is based on an a priori knowledgeabout the correspondence between the probability of a matching score anda certain probability that the displacement actually takes this value.In other words, there is a relation between the matching scoredistribution and the probability distribution which possesses in eachpoint of a likelihood that the feature is in that position or in thesmall neighborhood of it.

This means that a statistical correlation exists between the matchingscore and the feature presence in a given location. However thiscorrelation is loose, in the sense that no matching score can guaranteeeither the presence or the absence of the feature in a particularlocation. The type of disturbances for the matching score actually fallinto two large categories: First of all the imprecision, i.e. thematching score in the correct position may be smaller than the one ofone or more points on the neighborhood of the valid position. Anotherimprecision results from outliers, i.e. the matching score in thecorrect position is smaller than one or more points arbitrarily far fromthe valid position.

Based on the assumptions there is constructed an a priori knowledgeabout the conditional probability of a matching score under validdetection p(s/v). This probability function may be obtainedexperimentally, and it can either be stored in the look up table or itcan be approximated by an analytical function. In the present embodimentthe later is used as follows:

$\begin{matrix}{{p\left( {s/v} \right)} = {\max \left( {{a\frac{\left( {2 - s} \right)}{\alpha^{2}}{\exp \left( {- \left\lbrack \frac{1 - s}{\alpha} \right\rbrack^{2}} \right)}},p_{outlier}} \right)}} & (28)\end{matrix}$

Thereby p_(outlier), is the amount of probability that cannot bedismissed by any value of the matching score. Based on this correlationthe matching score map is then transformed into a probability map usingthe above relation (28) as follows:

P _(i)(x,y)=p(MS _(i)(x,y)), where i is the index of landmark  (29)

Then the probability map is normalized so that

$\begin{matrix}{{\sum\limits_{x,y}{P_{i}\left( {x,y} \right)}} = 1} & (30)\end{matrix}$

The matching score map therefore is transformed into a probability fieldas follows:

MS _(i) →PF _(i)  (31)

The probability fields of each side of the iris (left and right side):

$\begin{matrix}{{{Acc}_{L} = {\sum\limits_{i \in L}{PF}_{i}}},{{Acc}_{R} = {\sum\limits_{i \in R}{PF}_{i}}}} & (32)\end{matrix}$

It should be noted here that the “mathematically correct” implementationshould be

$\begin{matrix}{{{Acc}_{L} = {\prod\limits_{i \in L}{PF}_{i}}},{{Acc}_{R} = {\prod\limits_{i \in R}{PF}_{i}}}} & (33)\end{matrix}$

However, for convenience reasons the version shown in equation (32) isused. Then there is determined the maximum value and the maximumlocation in each accumulator, where s is the side (left or right fromthe iris):

x _(M) ^(S) ,y _(M) ^(S) ,p _(M) ^(S)

={x,y,Acc _(S)(x,y)|Acc _(S)(x,y)=max(Acc _(S))},  (34)

The actual displacement is obtained by taking into account just thelocation of the maximum.

Assuming that we are calculating in polar coordinates, and furtherassuming that the vertical coordinate (y) corresponds to the torsion,then the torsion can be determined by taking into account the verticalposition of the maximum location:

$\begin{matrix}{{{y_{M}^{L}T_{L}},{y_{M}^{R}T_{R}}}{{W_{L} = p_{M}^{L}},{W_{R} = p_{M}^{R}}}} & (35)\end{matrix}$

If |T_(L)|−|T_(R)|>1.5 deg then one may pick the most credible value(based on W_(R), W_(L))

If not, then

$\begin{matrix}{{T = \frac{{W_{L}T_{L}} + {W_{R}T_{R}}}{W_{L} + W_{R}}},} & (36)\end{matrix}$

The confidence may be computed as:

$\begin{matrix}{W = {\frac{1}{2}\left( {W_{L} + W_{R}} \right)}} & (37)\end{matrix}$

When calculating the probability field the imposition uncertainty may beincluded by computing for each position the accumulated probability ofits neighbors:

$\begin{matrix}{{{{PF}_{i}\left( {x^{c},y^{c}} \right)} = {\sum\limits_{x,y}{{P_{i}\left( {x,y} \right)}{\exp\left( {- \frac{\left( {x - x^{c}} \right)^{2} + \left( {y - y^{c}} \right)^{2}}{\sigma_{local}}} \right)}}}},{{for}\mspace{14mu} {every}\mspace{14mu} x^{c}},y^{c},} & (38)\end{matrix}$

thereby the parameter a is controlling the local uncertainty (forexample σ=3)

With the foregoing embodiment it is possible to calculate a displacementvalue which is not only based on the single part of the image but ratheron multiple image parts. The multiple regions used for displacementcalculation are based on landmark extraction, i.e. they have beenselected such that based on the features they contain are particularlysuitable for the purpose of eye registration or image tracking.

By using a correlation value which is computed by further taking intoaccount a weighting map it is made sure that those image areas whereblood vessels can be assumed to be present are particularly weighted andtherefore this enhances the correctness of the final result.

Moreover, by accumulating the multiple correlation values for themultiple regions of interest measurement errors and effects due toinstability of blood vessels are cancelled out, or in other words, bytaking the most likely displacement value the effect of these negativeinfluences can be kept small such that finally there is obtained a gooddisplacement value and eye registration or eye tracking can be performedvery well.

It will be clear to the skilled person that the aforementioneddescription explained the invention by means of illustrative embodimentsand that changes can be made without departing from the invention.

1. A system for registering and tracking the position of a person's eye,in particular for refractive ophthalmic surgery, comprising: a camerasystem for taking images of the person's eye; storage means connected tothe camera system for storing at least one eye image as a reference eyeimage; and an image processing system connected to at least one of thestorage means and the camera system, the image processing systemstructured to compare a momentary eye image with the reference eye imageand to output a signal representing a change of eye position between thereference eye image and the momentary eye image, wherein the system isstructured such that eye images containing at least the iris and thepupil of the eye are made at a first wavelength of light and that eyeimages containing scleral blood vessels are made at a different secondwavelength of light.
 2. A system according to claim 1, wherein the firstwavelength lies in a wavelength range between approximately 780 nm and950 nm and that the second wavelength lies in a wavelength range betweenapproximately 500 nm and 580 nm.
 3. A system according to claim 2,wherein the second wavelength is set to a local maximum of an absorptionspectrum of hemoglobin.
 4. A system according to claim 1 wherein itcomprises a first light source system illuminating the iris and thepupil of the eye and a second light source system illuminating thesclera of the eye.
 5. A system according to claim 1, wherein the firstand second light source systems are controlled such as to simultaneouslyilluminate the respective parts of the eye.
 6. A system according toclaim 1, wherein the light directing means comprise a scanning mirrorand/or a movable lens.
 7. A system according to claim 1, furthercomprising multiplexing means for controlling the first and second lightsource systems for alternately illuminating the entire eye.
 8. A systemaccording to claim 7, wherein the image processing system is structuredto subtract an image which is recorded at the second wavelength of lightfrom the second light source system from a preceding image recorded atthe first wavelength of light from the first light source system.
 9. Asystem according to claim 4, wherein the first light source system isarranged such as to illuminate the eye at an angle of approximately 30°with respect to the visual axis.
 10. A system according to claim 4,wherein the second light source system comprises two light sourcesarranged such as to symmetrically illuminate the eye at angles ofapproximately +35° and −35°, respectively, with respect to the visualaxis.
 11. A system according to claim 1, wherein the camera system isonly sensitive to light of the first and the second wavelength.
 12. Asystem according to claim 11, wherein the camera system comprises onesingle camera provided with a double-passband filter.
 13. A systemaccording to claim 11, wherein the camera system comprises a CCD camerahaving a combination chip with at least two separate color bandscorresponding to the first and the second wavelength, respectively. 14.A system according to claim 11, wherein the camera system comprises afirst camera sensitive only to the first wavelength and a second camerasensitive only to the second wavelength.